Bluetooth enabled portable communication device, such as mobile phones, smart phones, personal digital assistants (PDAs), pagers, MP3 players, cellular telephones, instant messaging devices, portable compact disk (CD) players, wireless email devices and the like devices, is used by an individual in daily life. These devices have various advanced functionalities like digital media transfer, internet usage etc. enabled by the use of Bluetooth, infrared and other ways of communication. An advance application available these days in the devices enables proximity estimation of similar devices in the surrounding environment. In addition to the estimation of proximity, the distance calculation of the other similar devices also plays a vital role in the pairing of the device, for various purposes like communication, and data transfer. A host of prior arts disclose various ways for calculating the distance of the Bluetooth enabled device in the proximity, when there is significant distance of separation between the devices.
A received signal strength indicator (RSSI) gives a direct measure of the shortest distance between the transmitter device and the receiver devices, wherein the signal strength and distance is inversely proportional, i.e. weaker the received signal strength, greater is the separation between transmitter (Tx) and receiver (Rx). In addition to the distance measure, if there is the use of three or more such Tx/Rx stations, it is possible to compute the exact location of a particular receiver (NODE) on a 2D plane.
However, the presence of obstacles, soft-furnishings, walls, ceiling etc. indoor result in significant spatial and temporal variation of the radio signal. This variation results in uncertain position estimation as well as distance calculation between two points or devices. When the Bluetooth enabled devices are present in a close environment say indoor environment and the distance of separation between the Bluetooth enabled devices is considerably small, typically less than 4 meters in a closed environment or in a small room (or workspace say less than 12 feet), the uncertainty in the distance calculation increases.
The existing models treat this spatial variation as a Lognormal random distribution and based on the standard Friis formula the relationship between received signal strength & distance is estimated. The existing models work with large potential errors in distance prediction and actual measurements in the closed environment, as the emphasis is on achieving reasonable accuracy in predication for bigger spaces like ware-house, large sized workspace etc. The existing techniques are not able to predict the distance correctly for indoor locations with an accuracy radius of less than +/−2 meters.
For functional based people-centric proximity estimation, the need for better accuracy arises. Functional based people-centric proximity estimation requires identifying and knowing which of the given set of people, wherein each person is uniquely identified by the Bluetooth device ID incorporated in their respective mobile phones, are interacting with each other. The interaction of given set of people with enabled devices results in following situation:                1. Two or more persons are positioned at places separated by say 1-2 m;        2. A small set of persons will be in relative motion for a brief period of time say coming close to each other, followed by a period where they are stationary with respect to each other that are interacting actively.        
Further, the present propagation models accept the fact that there will be more than 3-4 dB difference in RSSI prediction at a given distance and the actual measurements at a given point of time. That is why getting accuracy in indoor positioning still remains a significant challenge.
From the above discussed prior art it is observed that there is a need in the art to develop a system and method for better straight line distance estimation between two persons carrying Bluetooth enabled devices. Further there is a need to develop an accurate channel model for distance estimation that is accurate for short distances while retaining the functional requirements.